We present a platform to model and generate dynamic scenarios, and collect labeled sensor data with different sensor modalities.
To our knowledge, this is the first integrated platform for these tasks specialized to the autonomous driving domain.
We build on the Scenic scenario description language for modeling and generating scenarios [1]}, extended to dynamic scenarios in [2]}.
Scenic does not provide flexible features for generating sensor data, and in the context of data augmentation has only been used to generate static 2-dimensional RGB images [1]}, [4]}.
In this work, we add features to Scenic enabling it to serve as a dynamic scenario data generation platform, with the ability to: (1) configure arbitrary number and types of sensors on the ego vehicle in simulation as they would be mounted on a real autonomous car, (2) record data from these sensors along with corresponding ground-truth labels, (3) extract specific types of sensor data from a recorded simulation, and (4) visualize each type of recorded sensor data and labels. We open-source our platform [5]}, along with 340GB of labeled sensor data generated from example driving scenarios encoded in Scenic.
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